A variety of computing applications and networked systems are gaining popularity due to the ability to provide personalized services to users. However collecting data from human subjects to determine statistical associations that facilitate provision of personalized services can be particularly difficult. For example, many aspects of human behavior are associated with short time-scale neurological responses. High sampling rates are often needed in order to capture these events accurately. In turn, the high sampling rates often result in the collection of a significant amount of information. Questionnaire methods generally result in a large number of questions which can be time consuming and annoying for subjects to answer. As a consequence, the subjects may not answer the questions or may answer the questions in a hap-hazard or sloppy fashion, thereby degrading the quality of the collected data and potentially adversely impacting any subsequent statistical analysis.
Similarly, many human activities have a wide range of variations on a common theme, which can be difficult to capture in a succinct manner using a common questionnaire. For example, there are often a large number of permutations on the activities users perform throughout the day. Explicitly including all of these permutations can result in a very large number of potential answers that subjects may need to sort through when answering questions about their daily routines. This can be time consuming and annoying for the subjects. Thus, this approach to data collection may also result in questions that are not answered or hap-hazard or sloppy answers, which can also degrade the quality of the collected data and potentially adversely impact any subsequent statistical analysis.